Firing rate homeostasis counteracts changes in stability of recurrent neural networks caused by synapse loss in Alzheimer’s disease

Abstract

The impairment of cognitive function in Alzheimer's disease is clearly correlated to synapse loss. However, the mechanisms underlying this correlation are only poorly understood. Here, we investigate how the loss of excitatory synapses in sparsely connected random networks of spiking excitatory and inhibitory neurons alters their dynamical characteristics. Beyond the effects on the activity statistics, we find that the loss of excitatory synapses on excitatory neurons reduces the network's sensitivity to small perturbations. This decrease in sensitivity can be considered as an indication of a reduction of computational capacity. A full recovery of the network's dynamical characteristics and sensitivity can be achieved by firing rate homeostasis, here implemented by an up-scaling of the remaining excitatory-excitatory synapses. Mean-field analysis reveals that the stability of the linearised network dynamics is, in good approximation, uniquely determined by the firing rate, and thereby explains why firing rate homeostasis preserves not only the firing rate but also the network's sensitivity to small perturbations.

Brain Functional Network Architecture Reorganization and Alterations of Positive and Negative Affect, Experiencing Pleasure and Daytime Sleepiness in Cataract Patients after Intraocular Lenses Implantation

“…Meanwhile, recent studies suggest increased relative firing rate to be distinctive for progressive loss of synapses. It constitutes a coping, compensatory mechanism that allows one to avert the disruption of the functional network [61,62] In the present study, smaller postoperative eigenvector centrality in the left cerebellum VIII was associated with a higher level of positive affect. Cerebellum VIII is known for contributing to somatosensory and motor processing [63].…”

Subject-specific features of excitation/inhibition profiles in neurodegenerative diseases

“…Strong self-excitation was shown to be required in network models to achieve satisfactory simulations of decision making and working memory tasks 73 , in line with our evidence of high recurrent excitation in healthy controls. Moreover, in a previously proposed computational model applied to Alzheimer’s disease the variation of local recurrent excitation has been suggested as a brain mechanism employed to compensate for alterations induced by other types of synapse loss 74 . Unfortunately, nothing is known about recurrent excitation in network models of Frontotemporal Dementia or Amyotrophic lateral sclerosis, but the presence of Frontotemporal Dementia and Amyotrophic lateral sclerosis patients in cluster 3 prompts to explore the effect of this parameter in clinical conditions other than Alzheimer’s disease.…”

The Influence of Intraocular Lens Implantation and Alterations in Blue Light Transmittance Level on the Brain Functional Network Architecture Reorganization in Cataract Patients

“…Eigenvector centrality of the neuron is thought to positively correlate with its relative firing rate [ 44 ]. According to the spiking neural network model, increased firing rate constitutes a compensatory mechanism which prevents the disruption of neural network homeostasis after progressive loss of synapses [ 45 , 46 ]. The above results are congruent with the previous study reporting higher eigenvector centrality in participants with longer alcohol dependence [ 47 ].…”